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dc.contributor.authorKrawczak, Maciej
dc.date.accessioned2007-09-17T12:41:14Z
dc.date.available2007-09-17T12:41:14Z
dc.date.issued1997
dc.identifier.issn1134-5632
dc.identifier.urihttp://hdl.handle.net/2099/3498
dc.description.abstractThe supervised learning process of multilayer feedforward neural networks can be considered as a class of multi-objective, multi-stage optimal control problem. An iterative parametric minimax method is proposed in which the original optimization problem is embedded into a weighted minimax formulation. The resulting auxiliary parametric optimization problems at the lower level have simple structures that are readily tackled by efficient solution methods, such as the dynamic programming or the error backpropagation algorithm. The analytical expression of the partial derivatives of systems performance indices with respect to the weighting vector in the parametric minimax formulation is derived.
dc.format.extent195-202
dc.language.isoeng
dc.publisherUniversitat Politècnica de Catalunya. Secció de Matemàtiques i Informàtica
dc.relation.ispartofMathware & soft computing . 1997 Vol. 4 Núm. 3
dc.rightsReconeixement-NoComercial-CompartirIgual 3.0 Espanya
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.otherArtificial neural networks
dc.subject.otherSupervised learning
dc.subject.otherMulti-objective optimization
dc.subject.otherMinimax solution
dc.titleNeural networks learning as a multiobjective optimal control problem
dc.typeArticle
dc.subject.lemacProgramació (Matemàtica)
dc.subject.lemacSistemes de control intel·ligents
dc.subject.lemacAprenentatge automàtic
dc.subject.amsClassificació AMS::90 Operations research, mathematical programming::90C Mathematical programming
dc.rights.accessOpen Access


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